Carnegie Mellon University
The
Movement Primitives via Optimization

Anca Dragan, Katharina Muelling, J. Andrew (Drew) Bagnell, and Siddhartha Srinivasa
International Conference on Robotics and Automation (ICRA), May, 2015.


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Abstract
We formalize the problem of adapting a demonstrated trajectory to a new start and goal configuration as an optimization problem over a Hilbert space of trajectories: minimize the distance between the demonstration and the new trajectory subject to the new end point constraints. We show that the commonly used version of Dynamic Movement Primitives (DMPs) implement this minimization in the way they adapt demonstrations, for a particular choice of the Hilbert space norm. The generalization to arbitrary norms enables the robot to select a more appropriate norm for the task, as well as learn how to adapt the demonstration from the user. Our experiments show that this can significantly improve the robot’s ability to accurately generalize the demonstration.

Keywords
trajectory optimization, learning from demonstration, dynamic movement primitives

Notes
Associated Center(s) / Consortia: Quality of Life Technology Center, National Robotics Engineering Center, and Center for the Foundations of Robotics
Associated Lab(s) / Group(s): Personal Robotics

Text Reference
Anca Dragan, Katharina Muelling, J. Andrew (Drew) Bagnell, and Siddhartha Srinivasa, "Movement Primitives via Optimization," International Conference on Robotics and Automation (ICRA), May, 2015.

BibTeX Reference
@inproceedings{Dragan_2015_7923,
   author = "Anca Dragan and Katharina Muelling and J. Andrew (Drew) Bagnell and Siddhartha Srinivasa",
   title = "Movement Primitives via Optimization",
   booktitle = "International Conference on Robotics and Automation (ICRA)",
   month = "May",
   year = "2015",
}